| 摘要: |
| 针对墙参数模糊条件下运动目标成像速度慢、墙参数重建精度差等问题，提出了一种改进拟牛顿-粒子群优化(Limited Broyden Fletcher Goldfarb Shanno-Particle Swarm Optimization，LBFGS-PSO)算法，建立了LBFGS-PSO算法模型，解决了传统拟牛顿算法和粒子群算法计算速度慢、误差较大等问题。该算法与块正交匹配追踪（Block Orthogonal Matching Pursuit，BOMP）算法相结合不仅可以精确重建边墙位置，还能够准确地重建多径效应环境中的运动目标和静止目标，算法的计算速度和精度得到了一定程度的提高。仿真结果和数据分析验证了所提方法的性能。 |
| 关键词: 穿墙雷达；运动目标成像 多径效应 粒子群优化 块正交匹配追踪 |
| DOI： |
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| 基金项目: |
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| Through-the-Wall Radar Imaging for Moving Targets under Wall Parameter Uncertainties Condition |
| SUN Yanpeng,CHEN Li,QU Lele |
| (College of Electronic and Information Engineering,Shenyang Aerospace University,Shenyang 110136,China) |
| Abstract: |
| For the problems of slow imaging speed and poor reconstruction accuracy of moving targets with unknown wall parameters,an improved Limited Broyden Fletcher Goldfarb Shanno-Particle Swarm Optimization(LBFGS-PSO) algorithm is proposed.The model of LBFGS-PSO algorithm is established,which solves the problems of slow computation speed and large error of traditional quasi-Newton algorithm and PSO.Combining the algorithm with block orthogonal matching pursuit(BOMP) algorithm can not only accurately reconstruct the position of the side wall,but also use the multipath information to accurately reconstruct the moving target and the stationary target.The calculation speed and accuracy of the algorithm get a certain degree of improvement.Simulation results and data analysis verify the performance of the proposed method. |
| Key words: through-the-wall radar(TWR) moving target imaging multipath exploitation particle swarm optimization block orthogonal matching pursuit |
